Search

The integration of renewable energy sources, such as rooftop solar panels, into smart grids poses significant challenges for managing customer-side battery storage. In response, this paper introduces a novel reinforcement learning (RL) approach aimed at optimizing the coordination of these batteries. Our approach utilizes a single-agent, multi-environment RL system designed to balance power saving, customer satisfaction, and fairness in power distribution. The RL agent dynamically allocates charging power while accounting for individual battery levels and grid constraints, employing an actor–critic algorithm. The actor determines the optimal charging power based on real-time conditions, while the critic iteratively refines the policy to enhance overall performance. The key advantages of our approach include: (1) Adaptive Power Allocation: The RL agent effectively reduces overall power consumption by optimizing grid power allocation, leading to more efficient energy use. (2) Enhanced Customer Satisfaction: By increasing the total available power from the grid, our approach significantly reduces instances of battery levels falling below the critical state of charge (SoC), thereby improving customer satisfaction. (3) Fair Power Distribution: Fairness improvements are notable, with the highest fair reward rising by 173.7% across different scenarios, demonstrating the effectiveness of our method in minimizing discrepancies in power distribution. (4) Improved Total Reward: The total reward also shows a significant increase, up by 94.1%, highlighting the efficiency of our RL-based approach. Experimental results using a real-world dataset confirm that our RL approach markedly improves fairness, power efficiency, and customer satisfaction, underscoring its potential for optimizing smart grid operations and energy management systems. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid's stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers' privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers' activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection. [ABSTRACT FROM AUTHOR]

Aim This study was intended to compare syringe irrigation versus two irrigation agitation protocols [manual dynamic agitation (MDA) and passive ultrasonic irrigation] regarding postoperative pain (PP) after chemo-mechanical preparation of mandibular first molars with irreversible pulpitis and symptomatic apical periodontitis using numerical rating scale. Participants and methods Ninety patients with mandibular first molars diagnosed as symptomatic irreversible pulpitis with apical periodontitis were included in this study. Teeth were classified into three groups (n = 30) according to the final irrigation agitation protocol used. Root canals were irrigated using 5.25% sodium hypochlorite using the following protocols; group A: NaviTip (31-G 27 mm) with double side-port irrigator tip (side vented needle). Group B: MDA with well fitted master cone. Group C: ultrasonic activation using Endo ultra. PP was evaluated after 6, 12, 24, 48, and 72 h and 1 week. Level of confidence at 95% was used for statistical significance. Results Severity of PP was significantly more intense at 6-12 and 24-h time intervals in group B patients than groups A and C (P < 0.05). There was no significant difference among all groups at the other time intervals (P > 0.05). Conclusion After chemo-mechanical preparation in mandibular first molars with symptomatic irreversible pulpitis and apical periodontitis, MDA caused greater PP in comparison with the other irrigation agitation protocols in the first 24 h. Severity of PP in all groups decreased over time. [ABSTRACT FROM AUTHOR]

Background: MS is supposedly a T-Cell mediated autoimmune disorder of the central nervous system (CNS). Cytokines and other molecules involved in the regulation of apoptosis are thought to be of importance for the pathogenesis of MS. 1L-18 can direct auto-reactive and promote auto destruction in the CNS. Objective: To estimate the serum levels of 1L-18 in different MS clinical subtypes and to access their correlation to the disease activity, clinical grade of disability and lesion load of cerebral MRI. Methods: The study included 30 patients with definite MS: Ten remitting in remission (group I), ten relapsing - remitting in relapse (group II) and ten secondary progressive (group III) patients and ten, sex and age matched, healthy control subjects. The patients were evaluated clinically using expanded disability status scale (EDSS) and fatigue descriptive scale (FDS) and MRI brain - Serum levels of 1L - 18 were estimated for patients and controls. Results: The mean serum levels of 1L-18 were significantly elevated in all MS groups compared to control group. Group III patients had significantly higher serum 1L-18 levels than groups II and I. 1L-18 serum levels were significantly higher in group II compared to group I patient. Significant positive correlation was detected between mean serum levels of 1L-18 and mean EDSS and lesion load of cerebral MRI. Conclusion: Our results suggest involvement of 1L-18 immunopathogenesis of MS especially in active stages of the disease which correlated with degree of clinical disability and lesion load of cerebral MRI. So new therapeutic strategies of immunomodulator medications may be useful in MS. [ABSTRACT FROM AUTHOR]